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Activity Number: 167
Type: Contributed
Date/Time: Monday, August 4, 2014 : 10:30 AM to 12:20 PM
Sponsor: Section for Statistical Programmers and Analysts
Abstract #312241 View Presentation
Title: A Quantile-Based Convergence Diagnostic for MCMC
Author(s): Michael Lerch*+
Companies: Montana State University
Keywords: Bayesian Statistics ; MCMC ; convergence diagnostic
Abstract:

A critical part to fitting statistical models with MCMC techniques is to assess the convergence of the Markov chains. In many cases, the motivation to use MCMC is to produce posterior quantiles or intervals. We believe that assessing convergence of MCMC should be motivated by the type of value that will be reported. A different requirement may be imposed if the desired result is a mean, a quantile, or a mode. We present a strategy to assess MCMC convergence of posterior quantile estimates based on resampling of Markov chains to estimate the variability of the quantile estimates. We also show examples of use and comparison to other convergence diagnostics.


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